Modern machine learning methods require significant amounts of labelled data, making the preparation process time-consuming and resource-intensive. In this paper, we propose to consider the process of prototyping a tool for annotating and generating training datasets based on video tracking and segmentation. We examine different approaches to solving this problem, from technology selection through to final implementation. The developed prototype significantly accelerates dataset generation compared to manual annotation. All resources are available atthis https URL
View on arXiv@article{ivanov2025_2505.17884,
title={ Track Anything Annotate: Video annotation and dataset generation of computer vision models },
author={ Nikita Ivanov and Mark Klimov and Dmitry Glukhikh and Tatiana Chernysheva and Igor Glukhikh },
journal={arXiv preprint arXiv:2505.17884},
year={ 2025 }
}